Titanic tragedy data
Reading RAW training data
- Download the data set "Titanic_train.csv" from https://raw.githubusercontent.com/datasciencedojo/datasets/master/Titanic_train.csv
- Set working directory of R to the directory of the file using setwd()
titanic = read.csv("Titanic_train.csv")
Look at the first few rows
What would be some good features to consider here?
options(width = 110)
head(titanic)
## PassengerId Survived Pclass Name Sex Age SibSp Parch
## 1 1 0 3 Braund, Mr. Owen Harris male 22 1 0
## 2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 3 1 3 Heikkinen, Miss. Laina female 26 0 0
## 4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 5 0 3 Allen, Mr. William Henry male 35 0 0
## 6 6 0 3 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 S
## 6 330877 8.4583 Q
What is the data type of each column?
sapply(titanic,class)
## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket
## "integer" "integer" "integer" "factor" "factor" "numeric" "integer" "integer" "factor"
## Fare Cabin Embarked
## "numeric" "factor" "factor"
Converting class label to a factor
titanic$Survived = factor(titanic$Survived, labels=c("died", "survived"))
titanic$Embarked = factor(titanic$Embarked, labels=c("unkown", "Cherbourg", "Queenstown", "Southampton"))
sapply(titanic,class)
## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket
## "integer" "factor" "integer" "factor" "factor" "numeric" "integer" "integer" "factor"
## Fare Cabin Embarked
## "numeric" "factor" "factor"
str(titanic$Survived)
## Factor w/ 2 levels "died","survived": 1 2 2 2 1 1 1 1 2 2 ...
str(titanic$Sex)
## Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
Class distribution - PIE Charts
survivedTable = table(titanic$Survived)
survivedTable
##
## died survived
## 549 342
par(mar = c(0, 0, 0, 0), oma = c(0, 0, 0, 0))
pie(survivedTable,labels=c("Died","Survived"))
Is Sex a good predictor?
male = titanic[titanic$Sex=="male",]
female = titanic[titanic$Sex=="female",]
par(mfrow = c(1, 2), mar = c(0, 0, 2, 0), oma = c(0, 1, 0, 1))
pie(table(male$Survived),labels=c("Dead","Survived"), main="Survival Portion Among Men")
pie(table(female$Survived),labels=c("Dead","Survived"), main="Survival Portion Among Women")
Is Age a good predictor?
Age <- titanic$Age; summary(Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.42 20.12 28.00 29.70 38.00 80.00 177
How about summary segmented by survival
summary(titanic[titanic$Survived=="died",]$Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 21.00 28.00 30.63 39.00 74.00 125
summary(titanic[titanic$Survived=="survived",]$Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.42 19.00 28.00 28.34 36.00 80.00 52
Age distribution by Survival and Sex
par(mfrow = c(1, 2), mar = c(4, 4, 2, 2), oma = c(1, 1, 1, 1))
boxplot(titanic$Age~titanic$Sex, main="Age Distribution By Gender",col=c("red","green"))
boxplot(titanic$Age~titanic$Survived, main="Age Distribution By Survival",col=c("red","green"),
xlab="0:Died 1:Survived",ylab="Age")
Histogram of Age
hist(Age, col="blue", xlab="Age", ylab="Frequency",
main = "Distribution of Passenger Ages on Titanic")
Kernel density plot of age
d = density(na.omit(Age)) # density(Age) won't work, need to omit all NAs
plot(d, main = "kernel density of Ages of Titanic Passengers")
polygon(d, col="red", border="blue")
Comparison of density plots of Age with different Sex
Did Age have an impact on survival?
Create categorical groupings: Adult vs Child
An example of feature engineering!
## Multi dimensional comparison
Child <- titanic$Age # Isolating age.
## Now we need to create categories: NA = Unknown, 1 = Child, 2 = Adult
## Every age below 13 (exclusive) is classified into age group 1
Child[Child<13] <- 1
## Every child 13 or above is classified into age group 2
Child[Child>=13] <- 2
# Use labels instead of 0's and 1's
Child[Child==1] <- "Child"
Child[Child==2] <- "Adult"
# Appends the new column to the titanic dataset
titanic_with_child_column <- cbind(titanic, Child)
# Removes rows where age is NA
titanic_with_child_column <- titanic_with_child_column[!is.na(titanic_with_child_column$Child),]
Fare matters?
How about fare, ship class, port embarkation?
Diamond data
Overview of the diamond data
data(diamonds) # loading diamonds data set
head(diamonds, 16) # first few rows of diamond data set
## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47
## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53
## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49
## 10 0.23 Very Good H VS1 59.4 61 338 4.00 4.05 2.39
## 11 0.30 Good J SI1 64.0 55 339 4.25 4.28 2.73
## 12 0.23 Ideal J VS1 62.8 56 340 3.93 3.90 2.46
## 13 0.22 Premium F SI1 60.4 61 342 3.88 3.84 2.33
## 14 0.31 Ideal J SI2 62.2 54 344 4.35 4.37 2.71
## 15 0.20 Premium E SI2 60.2 62 345 3.79 3.75 2.27
## 16 0.32 Premium E I1 60.9 58 345 4.38 4.42 2.68
Histogram of carat
library(ggplot2)
ggplot(data=diamonds) + geom_histogram(aes(x=carat))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
Density plot of carat
ggplot(data=diamonds) +
geom_density(aes(x=carat),fill="gray50")
Scatter plots (carat vs. price)
ggplot(diamonds, aes(x=carat,y=price)) + geom_point()
Carat with colors
g = ggplot(diamonds, aes(x=carat, y=price)) # saving first layer as variable
g + geom_point(aes(color=color)) # rendering first layer and adding another layer
Carat with colors (more details)
g + geom_point(aes(color=color)) + facet_wrap(~color)
Let's consider cut and clarity
Your trun!
What is your knowledge of diamond's price after exploring this data?
